JP7345132B2 - Autonomous vacuum cleaner, autonomous vacuum cleaner control method, and program - Google Patents

Description

The present invention relates to an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space, a method for controlling the autonomous vacuum cleaner, and a program.

Patent Document 1 discloses a control program related to changing the route plan of an autonomous vacuum cleaner when an obstacle is detected in a cleaning space. When the autonomous vacuum cleaner disclosed in Patent Document 1 detects an obstacle after generating a travel route, the position of the obstacle is reflected in map information so as to avoid the position. Regenerate the driving route.

Patent No. 4157731

However, in the autonomous vacuum cleaner disclosed in Patent Document 1, for example, when an obstacle such as a carpet that can be overcome depending on the direction of travel is detected, when the user wants to clean the obstacle, The vehicle also runs while avoiding the obstacles in question.

The present invention provides an autonomous vacuum cleaner that can implement a cleaning method that is optimal for each obstacle.

An autonomous vacuum cleaner according to one aspect of the present invention is an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space, and the autonomous vacuum cleaner has obstacles in the vicinity of the autonomous vacuum cleaner. an obstacle detection section that detects information, a stack detection section that detects whether the autonomous vacuum cleaner is stuck on the obstacle, the obstacle information detected by the obstacle detection section, and the stack. Based on the detection result detected by the detection unit, calculate the number of times the autonomous vacuum cleaner gets stuck with respect to the obstacle, and link the obstacle information and the number of times information indicating the number of times it gets stuck. and a control section that controls the autonomous vacuum cleaner based on the obstacle information and the number of times information.

Further, a method for controlling an autonomous vacuum cleaner according to one aspect of the present invention is a method for controlling an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space, the method comprising: an obstacle detection step of detecting obstacle information indicating a state of the obstacle; a stuck detection step of detecting whether the autonomous vacuum cleaner is stuck on the obstacle; and a stuck detection step of detecting whether the autonomous vacuum cleaner is stuck on the obstacle; Based on the obstacle information and the detection result detected in the stuck detection step, calculate the number of times the object gets stuck, and store the obstacle information in association with the number of times information indicating the number of stuck times. and a control step of controlling the autonomous vacuum cleaner based on the obstacle information and the number of times information.

Further, a program according to one aspect of the present invention is a program for causing a computer to execute a method for controlling an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space, the program including: an obstacle detection step of detecting obstacle information indicating a state of the obstacle; a stuck detection step of detecting whether the autonomous vacuum cleaner is stuck on the obstacle; and a stuck detection step of detecting in the obstacle detection step. Based on the obstacle information obtained and the detection result detected in the stuck detection step, the number of times the object got stuck is calculated, and the obstacle information and the number information indicating the number of stuck times are linked. A method for controlling an autonomous vacuum cleaner comprising: an obstacle management step for storing in a storage unit; and a control step for controlling the autonomous vacuum cleaner based on the obstacle information and the number of times information. This is a program to be executed.

Note that the present invention may be realized as a non-temporary recording medium such as a computer-readable CD-ROM in which the above program is recorded. Further, the present invention may be realized as information, data, or a signal indicating the program. These programs, information, data, and signals may be distributed via a communication network such as the Internet.

According to an autonomous vacuum cleaner or the like according to one aspect of the present invention, it is possible to implement a cleaning method that is optimal for each obstacle.

FIG. 1 is a perspective view showing the appearance of an autonomous vacuum cleaner according to an embodiment. FIG. 2 is a bottom view showing the appearance of the autonomous vacuum cleaner according to the embodiment. FIG. 3 is a block diagram showing a characteristic functional configuration of the autonomous vacuum cleaner according to the embodiment. FIG. 4 is a diagram illustrating an example of an obstacle database held by the autonomous vacuum cleaner according to the embodiment. FIG. 5 is a diagram illustrating an example of a travel pattern database held by the autonomous vacuum cleaner according to the embodiment. FIG. 6 is a flowchart showing a process executed by the autonomous vacuum cleaner according to the embodiment. FIG. 7 is a flowchart showing details of the cleaning mode selection process shown in FIG. 6. FIG. 8 is a flowchart showing details of the stack count rewriting process shown in FIG. 6. FIG. 9 is a diagram illustrating an example of a cleaning mode of the autonomous vacuum cleaner according to the embodiment. FIG. 10 is a diagram illustrating an example of a cleaning mode of the autonomous vacuum cleaner according to the embodiment. FIG. 11 is a diagram illustrating an example of a cleaning mode of the autonomous vacuum cleaner according to the embodiment. FIG. 12 is a diagram illustrating an example of an image generated while the autonomous vacuum cleaner according to the embodiment is running. FIG. 13 is a diagram illustrating an example of a cleaning mode of the autonomous vacuum cleaner according to the embodiment. FIG. 14 is a diagram illustrating an example of a cleaning mode of the autonomous vacuum cleaner according to the embodiment. FIG. 15 is a block diagram showing a characteristic functional configuration of an autonomous vacuum cleaner according to a modification. FIG. 16 is a diagram illustrating an example of a cleaning mode database held by an autonomous vacuum cleaner according to a modification. FIG. 17 is a flowchart showing a first example of processing executed by the autonomous vacuum cleaner according to the modification. FIG. 18 is a flowchart showing a second example of processing executed by the autonomous vacuum cleaner according to the modification.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Below, embodiments of an autonomous vacuum cleaner and the like according to the present invention will be described in detail with reference to the drawings. It should be noted that the embodiments described below each represent a preferred specific example of the present invention. Therefore, the numerical values, shapes, materials, components, arrangement and connection forms of the components, steps, order of steps, etc. shown in the following embodiments are merely examples, and do not limit the present invention.

The accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present invention, and are not intended to limit the subject matter recited in the claims.

Furthermore, each figure is a schematic diagram and is not necessarily strictly illustrated. Furthermore, in each figure, substantially the same configurations are denoted by the same reference numerals, and overlapping explanations may be omitted or simplified.

Furthermore, in the following embodiments, expressions using "approximately", such as approximately triangular, are used. For example, "substantially triangular" means not only a complete triangle but also a substantially triangular shape, that is, a triangle with rounded corners, for example. The same applies to other expressions using "abbreviation".

In addition, in the following embodiments, when an autonomous vacuum cleaner that runs and cleans a floor in a predetermined space is viewed from vertically above, it is referred to as a top view, and when viewed from vertically below, it is referred to as a bottom view. May be stated.

(Embodiment)
[composition]
First, the configuration of an autonomous vacuum cleaner 100 according to an embodiment will be described with reference to FIGS. 1 to 5.

FIG. 1 is a perspective view showing the appearance of an autonomous vacuum cleaner 100 according to an embodiment. FIG. 2 is a bottom view showing the appearance of the autonomous vacuum cleaner 100 according to the embodiment.

The autonomous vacuum cleaner 100 is an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space.

First, the autonomous vacuum cleaner 100 runs around while capturing images of a predetermined space (more specifically, within the predetermined space) using the camera 60 or the like, thereby acquiring map information (data) representing a map of the predetermined space. ) is generated.

Next, the autonomous vacuum cleaner 100 calculates a travel route to be traveled when cleaning a predetermined space based on the generated map information. Next, the autonomous vacuum cleaner 100 cleans a predetermined space by traveling along the calculated travel route.

The autonomous vacuum cleaner 100 autonomously determines whether to avoid objects (obstacles) on the floor by observing the state of a predetermined space using a camera 60 and a sensor such as a cliff sensor. However, if an obstacle exists, the vehicle deviates from the calculated cleaning route and cleans by running while avoiding the obstacle.

The autonomous vacuum cleaner 100 uses, for example, SLAM (Simultaneous Localization and Mapping) to generate map information of a predetermined space to be cleaned and to determine the self-position of the autonomous vacuum cleaner 100 on the map indicated by the generated map information. Make an estimate.

The autonomous vacuum cleaner 100 includes, for example, a main body 10, two wheels 20, two side brushes 30, a laser range finder 40, a main brush 50, a camera 60, and a depth sensor 70. .

The main body 10 is a housing that accommodates each component of the autonomous vacuum cleaner 100. In this embodiment, the main body portion 10 has a substantially triangular shape when viewed from above. Note that the shape of the main body section 10 when viewed from above is not particularly limited. The top view shape of the main body portion 10 may be, for example, a substantially rectangular shape or a substantially circular shape. As shown in FIG. 2, the main body 10 has a suction port 11 on the bottom surface.

The two wheels 20 are wheels for driving the autonomous vacuum cleaner 100.

The side brush 30 is a brush that is provided on the lower surface of the main body 10 and is used to clean the floor of a predetermined space (hereinafter also simply referred to as the floor). In this embodiment, the autonomous vacuum cleaner 100 includes two side brushes 30. The number of side brushes 30 provided in the autonomous vacuum cleaner 100 may be one, or may be three or more, and is not particularly limited.

The laser range finder 40 is a sensor for measuring the distance between the autonomous vacuum cleaner 100 and objects, walls, etc. in a predetermined space. The laser range finder 40 is provided, for example, at the top of the main body 10. The laser range finder 40 is, for example, a so-called LIDAR (Light Detection and Ranging).

The main brush 50 is arranged at the suction port 11, which is an opening provided on the lower surface of the main body 10, and is a brush for sucking dust from the floor.

The camera 60 is an imaging device that is disposed in the main body 10 and generates an image by imaging a predetermined space.

The depth sensor 70 is, for example, a sensor that detects the distance between the autonomous vacuum cleaner 100 and a subject such as an obstacle included in the image generated by the camera 60. The depth sensor is, for example, an infrared sensor.

FIG. 3 is a block diagram showing a characteristic functional configuration of the autonomous vacuum cleaner according to the embodiment.

As shown in FIG. 3, the autonomous vacuum cleaner 100 includes a laser range finder 40, a camera 60, a depth sensor 70, a detection section (obstacle detection section) 110, a stack detection section 190, and an obstacle detection section 190. It includes a management section 120, a plan generation section 130, a control section 140, a storage section 150, a suction section 160, a drive section 170, and a cleaning section 180.

The detection unit 110 is a processing unit that acquires sensor data detected by the various sensors included in the autonomous vacuum cleaner 100, such as the laser range finder 40. The detection unit 110 is connected to the various sensors through control lines or the like so that sensor data can be obtained, for example. The detection unit 110 detects (obtains) obstacle information indicating the type of obstacles around the autonomous vacuum cleaner 100 . Here, the aspect of the obstacle is at least one of the type of the obstacle, the color of the obstacle, the position of the obstacle in a predetermined space, and the size of the obstacle. That is, the obstacle information is, for example, information indicating at least one of the type of the obstacle, the color of the obstacle, the position of the obstacle in a predetermined space, and the size of the obstacle as a mode of the obstacle. .

Note that the detection unit 110 identifies the type of obstacle (for example, a mat or a cord) by comparing it with a typical image of the obstacle stored in advance in the storage unit 150, and detects the identified obstacle. may be output as obstacle information.

The detection unit 110 includes, for example, an obstacle type detection unit 111, a distance detection unit 112, a relative position detection unit 113, a self-position detection unit 114, and an obstacle coordinate detection unit 115.

The obstacle type detection unit 111 acquires an image generated by the camera 60 (that is, the image is input), and analyzes the image to detect the type of obstacle included in the image. The obstacle type detection unit 111 detects, for example, the time when the image was generated from the camera 60, numerical values indicating R (Red), G (Green), and B (Blue) of the image, and an identification number indicating the position in the image. (pixel number), that is, RGB values for each pixel, are acquired from the camera 60 and image analyzed to detect the type of obstacle, the color of the obstacle, and the like.

In addition, the obstacle type detection unit 111 sends the image to the distance detection unit 112 and the relative position detection unit 113 using the obstacle information indicating the type of the obstacle and the color of the obstacle as the obstacle type. It is output in association with time information indicating the time the image was taken.

In addition, the obstacle type detection unit 111 calculates the bounding box (circumscribed rectangular frame surrounding the obstacle) of the obstacle included in the image, and the position of the underline of the calculated bounding box (more specifically, the pixel position ) is output to the distance detection unit 112.

The obstacle type detection unit 111 also sends information indicating the calculated center position (more specifically, pixel position) of the bounding box, the number of vertical pixels, the number of horizontal pixels, etc. of the bounding box to the distance detection unit 112. Output.

The distance detection unit 112 detects (that is, measures or calculates) the distance between the autonomous vacuum cleaner 100 and the obstacle detected by the obstacle type detection unit 111 and included in the image generated by the camera 60. The distance detection unit 112 detects, for example, the distance between the obstacle detected by the depth sensor 70 and the autonomous vacuum cleaner 100 included in the image. For example, the storage unit 150 stores in advance position information indicating the installation position of the camera 60 in the main body 10, size information indicating the size of the main body 10, and the like. The distance detection unit 112 detects the distance (more specifically, the closest distance, which is the closest distance) between the main body 10 and the obstacle included in the image based on this information. The distance detection unit 112 outputs distance information indicating the detected distance to the plan generation unit 130 in association with the time when the image was taken, obstacle information, etc.

The relative position detection unit 113 acquires the distance (relative distance) for each pixel of the image from the depth sensor 70 (that is, information indicating the distance is input), and autonomously detects the obstacle detected by the obstacle type detection unit 111. The relative position of the obstacle detected by the depth sensor 70 with respect to the traveling vacuum cleaner 100 is detected. For example, the relative position detection unit 113 detects the position (relative position) of the obstacle at predetermined coordinates when the position of the autonomous vacuum cleaner 100 is set as the origin. The relative position detection unit 113 outputs the detected relative position to the obstacle coordinate detection unit 115 in association with the time when the image was taken, obstacle information, the relative distance detected by the depth sensor 70, and the like.

The self-position detection unit 114 detects the position of the autonomous vacuum cleaner 100 in a predetermined space. The self-position detection unit 114 detects the position based on, for example, the distance to objects, including obstacles, walls, etc., located around the autonomous vacuum cleaner 100, which is input from the laser range finder 40, and the generated map information. Then, the coordinates of the autonomous vacuum cleaner 100 on the map indicated by the map information are calculated. The self-position detection unit 114 links the self-position information indicating the detected self-position with the time of detection, etc., and sends it to the obstacle coordinate detection unit 115, the stack detection unit 190, the obstacle management unit 120, and the plan generation unit 130. Output.

The obstacle coordinate detection unit 115 calculates a map based on the relative position of the object with respect to the autonomous vacuum cleaner 100 detected by the relative position detection unit 113 and the position of the autonomous vacuum cleaner on the map detected by the self-position detection unit 114. Detect the location of obstacles in the area. The obstacle coordinate detection unit 115 associates position information indicating the position of the detected obstacle with type information indicating the type of the obstacle and outputs it to the obstacle management unit 120 as obstacle information.

The stuck detection unit 190 is a processing unit that detects whether the autonomous vacuum cleaner 100 is stuck on an obstacle. Here, the term "stuck" refers to a state in which the autonomous vacuum cleaner 100 runs onto an obstacle and cannot move. The stuck detection unit 190 determines whether the autonomous vacuum cleaner 100 is stuck based on drive information indicating the driving state of the drive unit 170 and the self-position of the autonomous vacuum cleaner 100 detected by the self-position detection unit 114. Detect. The stuck detection unit 190 determines that the autonomous vacuum cleaner 100 is stuck, for example, when there is no change in the self-position even though the drive unit 170 has been driven for a predetermined period of time. The self-position detection unit 114 outputs the detection result, that is, stack information indicating whether the autonomous vacuum cleaner 100 is stuck, to the obstacle management unit 120.

Note that the information input/output by the detection unit 110, stack detection unit 190, etc. described above is an example, and information other than the above input/output for outputting obstacle information to the obstacle management unit 120 and plan generation unit 130 is may be executed.

In addition to the laser range finder 40, the camera 60, and the depth sensor 70, the autonomous vacuum cleaner 100 also includes various sensors such as a laser range finder 40, a camera 60, and a depth sensor 70. A cliff sensor that measures the distance from the autonomous vacuum cleaner 100, a slip sensor that detects the movement of the autonomous vacuum cleaner 100, an ultrasonic sensor that detects the distance from the autonomous vacuum cleaner 100 to an arbitrary object, etc. may be provided. Furthermore, for example, the autonomous vacuum cleaner 100 may include a sensor for detecting odometry information used to calculate the direction in which the autonomous vacuum cleaner 100 moves.

The obstacle management unit 120 calculates the number of times the autonomous vacuum cleaner 100 gets stuck on the obstacle based on the obstacle information and the detection result detected by the stuck detection unit 190, and calculates the number of times the autonomous vacuum cleaner 100 gets stuck on the obstacle, and combines it with the obstacle information. This is a processing unit that stores the information in the storage unit 150 in association with the number of times information indicating the number of times the stack has occurred. Specifically, the obstacle management unit 120 generates an obstacle database 151 indicating the type of obstacle detected by the detection unit 110 and its position on a map showing a predetermined space, and stores it in the storage unit 150. If the obstacle database 151 is stored in the storage unit 150 in advance, the obstacle management unit 120 updates the obstacle database 151 based on the detection result by the detection unit 110.

The storage unit 150 is a storage device that stores an obstacle database 151 and a driving pattern database 152.

The obstacle database 151 is table information containing information on obstacles in a predetermined space.

FIG. 4 is a diagram showing an example of the obstacle database 151 held by the autonomous vacuum cleaner 100 according to the embodiment.

The obstacle database 151 includes, for example, ID information 151A, obstacle information 151F, and frequency information 151E. Obstacle information 151F includes type information 151B and position information 151C and 151D. The type information 151B and the position information 151C and 151D are each examples of obstacle information.

ID information 151A is an identifier for identifying each obstacle. In this embodiment, a numerical value such as "0001" is shown as the ID information 151A.

Type information 151B is information indicating the type of obstacle. In this embodiment, the type information 151B includes mat, cord, level difference, and the like. The type of obstacle may be not only an object such as a mat but also a part of a structure such as a step.

The position information 151C, 151D is information indicating the position of the corresponding obstacle. In this embodiment, the position information includes the approximate center position of the rectangle of the obstacle as the X coordinate (position information 151C) and Y coordinate (position information 151D).

The number of times information 151E is information indicating the number of times the autonomous vacuum cleaner 100 gets stuck on the corresponding obstacle.

For example, ID:0001 included in the ID information 151A of the obstacle database 151 includes a mat as the type of obstacle, and (X, Y) = (500, 500) as the center position (XY coordinates) of the mat. The number of stacks is 0 times. For example, when the autonomous vacuum cleaner 100 gets stuck on an obstacle with (X, Y) = (500, 500), the obstacle management unit 120 stores the number of times the mat with ID: 0001 in the obstacle database 151 is used. Update the number of stacks included from 0 to 1.

The obstacle management unit 120 outputs to the plan generation unit 130 obstacle information indicating the type of obstacle, etc., and frequency information indicating the number of times the autonomous vacuum cleaner stacks 100 times with respect to the obstacle.

Referring again to FIG. 3, the plan generation unit 130 is a processing unit that generates plan information indicating the cleaning mode of the autonomous vacuum cleaner 100 based on the obstacle information 151F and the number of times information 151E. For example, it is a processing unit that generates a cleaning plan (plan information) indicating how the autonomous vacuum cleaner 100 travels and cleans a predetermined space.

For example, the plan generation unit 130 acquires map information indicating a map of a predetermined space. The plan generation unit 130 generates map information using SLAM, for example, based on various sensors included in the autonomous vacuum cleaner 100 and self-location information indicating the self-location from the self-location detection unit 114. The autonomous vacuum cleaner 100 may include a communication interface (not shown) for communicating with an external communication device, and acquire map information from the communication device via the communication interface. The map information may be stored in the storage unit 150 in advance. The plan generation unit 130 also determines the driving route of the autonomous vacuum cleaner 100, specifically, the control method of the drive unit 170, such as the rotation speed of the wheel motor 171 and the orientation of the wheels 20, based on the map information. Generate planning information that determines the driving method. The plan generation unit 130 generates plan information that determines a travel route for the autonomous vacuum cleaner 100 to travel, based on the obstacle information 151F and the number of times information 151E, for example. Specifically, for example, the plan generation unit 130 generates plan information that determines the driving method of the drive unit 170 based on the number of times information 151E.

The plan generation unit 130 also determines the control method of the suction unit 160 (for example, the suction force, more specifically, the rotation speed of the suction motor 161), the rotation speed of the wheel motor 171, the orientation of the wheels 20, etc. Plan information is generated that indicates a cleaning method including a control method for the cleaning unit 180 and a control method for the cleaning unit 180 (for example, the number of rotations of the brush motor 181). The plan generation unit 130 generates plan information in which the suction force of the suction unit 160 (specifically, the rotation speed of the suction motor 161) is determined based on the obstacle information 151F and the number of times information 151E, for example. Further, for example, the plan generation unit 130 generates plan information in which the number of rotations of the brush motor 181 is determined based on the obstacle information 151F and the number of times information 151E.

In this way, the plan generation unit 130 determines how to run and clean the obstacle detected by the detection unit 110, that is, the cleaning mode indicating the cleaning method and the running method, and performs the determined cleaning. Plan information including control details for causing the control unit 140 to control the autonomous vacuum cleaner 100, more specifically, the suction unit 160, the drive unit 170, and the cleaning unit 180, is generated so as to achieve the desired mode.

The control unit 140 is a processing unit that controls the autonomous vacuum cleaner 100 based on the obstacle information 151F and the number of times information 151E. Specifically, the control unit 140 controls the suction unit 160, the drive unit 170, and the cleaning unit 180 included in the autonomous vacuum cleaner 100 based on the obstacle information 151F and the number of times information 151E. More specifically, the control unit 140 controls the suction unit 160, the drive unit 170, and the cleaning unit 180 based on the plan information generated by the plan generation unit 130, thereby making the autonomous vacuum cleaner 100 The device autonomously runs and cleans a predetermined space. In the present embodiment, the control unit 140 controls the suction unit 160, the drive unit 170, and the cleaning unit 180 based on the obstacle database 151 calculated by the obstacle management unit 120 and the travel pattern database 152. .

FIG. 5 is a diagram illustrating an example of the travel pattern database 152 held by the autonomous vacuum cleaner 100 according to the embodiment.

The travel pattern database 152 includes type information 152A, frequency information 152B, and cleaning mode information 152C.

Type information 152A is information indicating the type of obstacle.

The number of times information 152B is information indicating the number of times the autonomous vacuum cleaner 100 gets stuck on an obstacle.

The cleaning mode information 152C indicates the cleaning mode of the autonomous vacuum cleaner 100 with respect to the corresponding obstacle when the number of times the autonomous vacuum cleaner 100 gets stuck on the corresponding obstacle is the number of times the autonomous vacuum cleaner 100 gets stuck on the corresponding obstacle. This is the information shown. Note that in the present embodiment, the cleaning mode information 152C shows only the running method (running pattern), that is, the control method of the drive unit 170 controlled by the control unit 140; Information indicating the cleaning method, such as the rotation speed of the suction motor 161 provided in the section 160 and the rotation speed of the brush motor 181 provided in the cleaning section 180 controlled by the control section 140, may be included.

For example, when the type of obstacle is a mat, the autonomous vacuum cleaner 100 moves straight to the mat at normal speed and runs over the mat when the number of stacks is 0. For example, when the type of obstacle is a mat, when the number of stacks is 1, the autonomous vacuum cleaner 100 moves straight onto the mat at 1.5 times the normal speed for 1 second and runs onto the mat. do. For example, if the type of obstacle is a mat, and the number of stacks is 2, the autonomous vacuum cleaner 100 runs in a circular arc with a radius of 30 cm on the mat at 1.5 times the normal speed for 1 second. In other words, the vehicle runs while turning and runs over the vehicle. For example, when the type of obstacle is a mat, the autonomous vacuum cleaner 100 avoids (for example, turns back) and runs without running onto the mat when the number of stacks is five.

As described above, the control unit 140 controls the autonomous vacuum cleaner 100 (more specifically, the suction unit 160, The drive section 170 and the cleaning section 180) are controlled. More specifically, the plan generation unit 130 determines a cleaning mode for the obstacle based on the obstacle database 151 and the driving pattern database 152 generated by the obstacle management unit 120, and uses the determined cleaning mode. Thus, the control section 140 generates the plan information so as to cause the suction section 160, the driving section 170, and the cleaning section 180 to do so.

Note that the obstacle may be any object that may hinder the movement of the autonomous vacuum cleaner 100, and may be a part of the floor of a building, such as a step. For example, when the type of obstacle is a step, the autonomous vacuum cleaner 100 moves straight in the opposite direction and climbs onto the step when the number of stacks is one.

Moreover, although an example has been described above in which the cleaning mode information 152C of the driving pattern database 152 includes an action of running over or an action of avoiding an obstacle, the present invention is not limited to this. For example, in the running pattern database 152, the type of obstacle and the number of times the obstacle gets stuck are associated with the running pattern (that is, the cleaning mode) for an action that may cause a stack, such as the action of getting down from an obstacle. good.

Various processing units such as the detection unit 110, the obstacle management unit 120, the plan generation unit 130, and the control unit 140 include, for example, a control program for executing the above-described processing, and a CPU that executes the control program. (Central Processing Unit). Various processing units such as the detection unit 110, the obstacle management unit 120, the plan generation unit 130, and the control unit 140 may be realized by one or more CPUs.

The storage unit 150 is a memory that stores an obstacle database 151 and a driving pattern database 152. The storage unit 150 is realized by, for example, an HDD (Hard Disk Drive), a flash memory, or the like. Furthermore, the storage unit 150 stores, for example, control programs executed by various processing units such as the control unit 140.

The suction unit 160 is a mechanism for suctioning dust on the floor surface of a predetermined space by suctioning the floor surface. The suction unit 160 includes, for example, a suction motor 161.

A suction motor 161 is a motor that is connected to a fan and sucks up dust on the floor by rotating the fan.

The drive unit 170 is a mechanism for driving the autonomous vacuum cleaner 100. The drive unit 170 includes, for example, a wheel motor 171.

The wheel motor 171 is a motor that is connected to the wheel 20 and rotates the wheel 20.

The autonomous vacuum cleaner 100 can move freely, such as going straight, going backwards, turning left, turning right, etc., by independently controlling the rotations of the two wheels 20 that the drive unit 170 has. Note that the autonomous vacuum cleaner 100 may further include wheels (auxiliary wheels) that are not rotated by the wheel motor 171.

The cleaning unit 180 is a mechanism for cleaning the floor surface. The cleaning unit 180 includes, for example, a brush motor 181.

The brush motor 181 is a motor that is connected to brushes such as the main brush 50 and drives (rotates) the brushes such as the main brush 50.

[Processing procedure]
<Summary>
Next, an overview of the processing procedure of the autonomous vacuum cleaner 100 will be described with reference to FIG. 6.

FIG. 6 is a flowchart showing a process executed by autonomous vacuum cleaner 100 according to the embodiment.

First, the autonomous vacuum cleaner 100 receives a cleaning start signal (step S101). For example, the autonomous vacuum cleaner 100 includes a receiving section (not shown). The user operates a console or the like to transmit a cleaning start signal to the autonomous vacuum cleaner 100 indicating that cleaning is to be started. The autonomous vacuum cleaner 100 receives the cleaning start signal at the receiving unit. The receiving unit is, for example, an optical sensor for receiving a signal, a communication interface such as a wireless communication circuit when the signal is transmitted from a communication device such as a smartphone, or the like. Note that the autonomous vacuum cleaner 100 may have an operation section such as a button attached to the main body section 10 instead of the reception section and used to obtain instructions from the user. The autonomous vacuum cleaner 100 may acquire a cleaning start signal when the operation unit is operated by the user.

Next, the plan generation unit 130 generates plan information (step S102). For example, the plan generation unit 130 calculates a travel route based on map information indicating a map of a predetermined space stored in the storage unit 150, and the suction unit 160, drive unit 170, and By controlling the cleaning unit 180, plan information for causing the autonomous vacuum cleaner 100 to travel and clean is generated. Note that if map information does not exist in the storage unit 150, for example, the plan generation unit 130 causes the control unit 140 to cause the autonomous vacuum cleaner 100 to travel in a predetermined space, and generates a map showing a map of the predetermined space using SLAM. Information may be generated.

Next, the control unit 140 controls the suction unit 160, the drive unit 170, and the cleaning unit 180 based on the plan information generated by the plan generation unit 130 to cause the autonomous vacuum cleaner 100 to run and clean. (Step S103).

Next, the plan generation unit 130 determines whether cleaning is completed (step S104). For example, the plan generation unit 130 calculates the trajectory of the route traveled by the autonomous vacuum cleaner 100 based on the detection result of the self-position detection unit 114, and the calculated trajectory matches the travel route indicated by the plan information. In other words, it is determined whether the entire travel route indicated by the plan information has been traveled.

When the plan generation unit 130 determines that the cleaning plan is completed (Yes in step S104), the control unit 140 drives the drive unit 170 to move the autonomous vacuum cleaner 100 to the initial position where it started traveling. Return and end the process.

On the other hand, if the plan generation unit 130 determines that the cleaning has not been completed (No in step S104), the obstacle management unit 120 determines whether the stuck detection unit 190 has detected the autonomous vacuum cleaner 100 to be stuck. (Step S105). Specifically, the stuck detection unit 190 detects whether the autonomous vacuum cleaner 100 is stuck on an obstacle.

If the stack detection unit 190 determines that the autonomous vacuum cleaner 100 is stuck (Yes in step S105), the obstacle management unit 120 acquires the position of the autonomous vacuum cleaner 100 from the self-position detection unit 114. Then, for the obstacle existing at the acquired position, the number of stacks included in the number of times information 151E of the obstacle database 151 is rewritten (that is, updated) (step S106).

On the other hand, if the obstacle management unit 120 determines that the stack detection unit 190 has not detected the stack of the autonomous vacuum cleaner 100 (No in step S105), the plan generation unit 130 determines that the detection unit 110 It is determined whether or not it has been detected (step S107). Specifically, the detection unit 110 detects obstacle information indicating the type of obstacles around the autonomous vacuum cleaner 100.

When it is determined that the detection unit 110 has detected an obstacle (Yes in step S107), the plan generation unit 130 refers to the driving pattern database 152 based on the obstacle information indicating the type of the detected obstacle. A cleaning mode for the obstacle is selected (step S108), and the process returns to step S102. Specifically, the plan generation unit 130 obtains the number of stuck times for the obstacle based on the obstacle database 151, and obtains the cleaning mode for the number of stuck times for the obstacle based on the driving pattern database 152. Next, in step S102, the plan generation unit 130 generates plan information that causes the control unit 140 to control the cleaning mode acquired in step S108. Next, in step S103, the control unit 140 generates a plan generated by the plan generation unit 130 based on the obstacle information 151F and the number of times information 151E, more specifically, based on the obstacle information 151F and the number of times information 151E. The autonomous vacuum cleaner 100 is controlled based on the plan information.

On the other hand, if the plan generation unit 130 determines that the detection unit 110 has detected an obstacle (Yes in step S107), the control unit 140 returns the process to step S103.

By repeating the above steps, each time the autonomous vacuum cleaner 100 gets stuck on an obstacle, the obstacle management unit 120 generates the obstacle information 151F indicating the state of the obstacle detected in step S107 and the obstacle information 151F detected in step S105. Based on the detection result, the number of stuck times for the obstacle is calculated, and the obstacle information 151F and the number of times information 151E are stored in the storage unit 150 in association with each other (step S106).

<Cleaning mode selection process>
Next, details of the cleaning mode selection process (step S108) shown in FIG. 6 will be described with reference to FIG.

FIG. 7 is a flowchart showing details of the cleaning mode selection process (step S108) shown in FIG.

First, regarding the obstacle detected by the obstacle type detection unit 111, the relative position detection unit 113 determines the relationship between the autonomous vacuum cleaner 100 and the object based on the detected position of the obstacle with respect to the autonomous vacuum cleaner 100. A distance (relative distance) is calculated (step S201).

Next, the obstacle coordinate detection unit 115 calculates the position coordinates of the object with respect to predetermined coordinate axes on a map indicating a predetermined space (step S202).

Next, the obstacle management unit 120 determines whether the obstacle whose position coordinates are detected by the obstacle coordinate detection unit 115 is an obstacle registered in the obstacle database 151 (step S203).

If the obstacle management unit 120 determines that the obstacle at the position coordinates detected by the obstacle coordinate detection unit 115 is an obstacle registered in the obstacle database 151 (Yes in step S203), the obstacle management unit 120 stores the obstacle in the obstacle database. 151, the number information 151E indicating the number of times the autonomous vacuum cleaner 100 stacks against the obstacle is acquired, and the acquired number information 151E is output to the plan generation unit 130 (step S204).

On the other hand, if the obstacle management unit 120 determines that the obstacle at the position coordinates detected by the obstacle coordinate detection unit 115 is not an obstacle registered in the obstacle database 151 (No in step S203), the obstacle management unit 120 Information about the obstacle is added to the database 151 (step S205).

Next, the plan generation unit 130 selects and determines a cleaning mode for the obstacle based on the acquired frequency information 151E and the travel pattern database 152 (step S206).

<Stack count rewriting process>
Next, details of the stack count rewriting process (step S106) shown in FIG. 6 will be described with reference to FIG. 8.

FIG. 8 is a flowchart showing details of the stack count rewriting process (step S106) shown in FIG.

First, when the stack detection unit 190 detects that the autonomous vacuum cleaner 100 is stuck, the self-position detection unit 114 detects the stuck position by detecting the self-position of the autonomous vacuum cleaner 100 (step S301).

Next, the obstacle management unit 120 determines whether there is an obstacle registered in the obstacle database 151 at the same position as the position detected by the self-position detection unit 114 (step S302).

When the obstacle management unit 120 determines that there is an obstacle registered in the obstacle database 151 at the same position as the position detected by the self-position detection unit 114 (Yes in step S302), in the obstacle database 151, The number of stacks corresponding to the obstacle at the same position as the position detected by the self-position detection unit 114 is added by 1 (step S303).

On the other hand, if the obstacle management unit 120 determines that there is no obstacle registered in the obstacle database 151 at the same position as the position detected by the self-position detection unit 114 (No in step S302), the process ends. .

Note that the obstacle management unit 120 acquires the position of the autonomous vacuum cleaner 100 from the self-position detection unit 114, and if there is no obstacle in the obstacle database 151 at the acquired position (that is, No in step S302). ), a new obstacle may be added to the obstacle database 151. If the type of obstacle cannot be specified, the obstacle management unit 120 may update the obstacle database 151 while leaving the type information 151B blank.

[Specific examples of cleaning methods]
Next, specific examples of cleaning modes of the autonomous vacuum cleaner 100 will be described with reference to FIGS. 9 to 14.

9 to 11, FIG. 13, and FIG. 14 are diagrams showing examples of cleaning modes of the autonomous vacuum cleaner 100 according to the embodiment. Note that FIGS. 9 to 11, FIG. 13, and FIG. 14 are diagrams showing cases in which the autonomous vacuum cleaner 100 performs processing such as traveling in this chronological order. 9 to 11, FIG. 13, and FIG. 14 are schematic diagrams of the predetermined space CA viewed from above. Further, in this specific example, it is assumed that a mat 220A, which is an example of an obstacle, exists in the predetermined space CA.

As shown in FIG. 9, for example, when receiving a cleaning start signal, the plan generation unit 130 generates a travel route PP indicated by a broken line arrow based on map information indicating a map of a predetermined space CA. The plan generation unit 130 also calculates coordinates with the origin at the place where the travel is to start.

Note that if the autonomous vacuum cleaner 100 does not hold map information, it generates map information by SLAM while running around in the predetermined space CA. In that case, the plan generation unit 130 may set, for example, the position of the charger 210 as the origin of the coordinates.

In this specific example, the autonomous vacuum cleaner 100 holds map information in advance (that is, stored in the storage unit 150), is connected to the charger 210, and is connected to the charger 210 at a location connected to the charger 210. Assume that the origin is

Further, for example, the plan generation unit 130 sets the direction of movement as the positive X-axis direction, and sets the direction perpendicular to the X-axis as the Y-axis direction. In this case, before the autonomous vacuum cleaner 100 starts traveling, its own position is (X, Y, direction of travel) = (0 m, 0 m, 0°).

Note that X in (X, Y, direction of movement) includes, for example, the distance from the origin in the X-axis direction. Further, for example, Y in (X, Y, direction of movement) includes the distance from the origin in the Y-axis direction. Further, for example, the direction of movement (X, Y, direction of movement) includes the angle at which the autonomous vacuum cleaner 100 moves with respect to the positive direction of the X-axis.

As shown in FIG. 10, it is assumed that the autonomous vacuum cleaner 100 travels along a trajectory T from the state shown in FIG. 9 along the travel route. For example, the autonomous vacuum cleaner 100 (more specifically, the detection unit 110) continues to travel while photographing the photographing range V with the camera 60 and determining whether or not an obstacle exists. For example, the self-position of the autonomous vacuum cleaner 100 shown in FIG. 10 is (X, Y, direction of travel) = (0.6 m, 0.6 m, 180°).

As shown in FIG. 11, it is assumed that the camera 60 photographs the mat 220A, for example. In other words, assume that the detection unit 110 detects the mat 220A.

FIG. 12 is a diagram illustrating an example of an image 200 generated while the autonomous vacuum cleaner 100 according to the embodiment is running.

In FIG. 12, a mat 220B, which is an image of the mat 220A, is reflected. The detection unit 110 generates a bounding box BB that is a roughly rectangular shape of the mat 220B in the image 200, and calculates the center of the generated bounding box BB as the position of the mat 220B. Furthermore, the detection unit 110 calculates the position of the mat 220A located in the predetermined space CA from the position of the mat 220B in the image 200. Here, the detection unit 110 uses the time information indicating the time when the image was generated by the camera and the obstacle information as (shooting date and time, type, relative distance, orientation) = (201906131145, mat, 0.9 m, 25 °) is detected. For example, the shooting date and time includes a number string indicating 11:45 on June 13, 2019. Furthermore, the relative distance includes the closest distance from the position of the mat 220A corresponding to the center of the bounding box BB to the autonomous vacuum cleaner 100 (more specifically, the main body 10). The positioning direction includes the angle between the direction in which the autonomous vacuum cleaner 100 moves and the direction in which the mat 220A is positioned, which corresponds to the center of the bounding box BB as seen from the autonomous vacuum cleaner 100. .

Note that whether or not the control unit 140 executes control for the obstacle, more specifically, whether or not the plan generation unit 130 generates plan information based on the number of times information for the obstacle, depends on the image 200. It is not limited to whether or not an obstacle is included in the image. For example, if an obstacle is reflected in the image 200 between two predicted passing lines 230 indicated by dashed lines, which the autonomous vacuum cleaner 100 is scheduled to pass, the plan generation unit 130 and the control unit 140 Additionally, the above-described processing for obstacles may be executed.

As shown in FIG. 13, the detection unit 110 calculates the relative coordinates (relative distance, orientation) of the mat 220A from the image 200 shown in FIG. 12 (0.9 m, 25°). The detection unit 110 also calculates its own position. In the specific example shown in FIG. 13, the self-position is assumed to be (X, Y, direction of travel) = (-0.5 m, 1.2 m, 0°). Furthermore, the detection unit 110 calculates the positional coordinates of the mat 220A (in other words, the absolute coordinates from the origin) from the relative coordinates of the mat 220A and the self-position. In this specific example, the position coordinates of the mat 220A are (-0.5+0.9×cos25°, 1.2+sin25°). The obstacle management unit 120 determines whether an obstacle matching the position coordinates of the mat 220A is registered in the obstacle database 151. Here, for example, it is assumed that an obstacle that matches the position coordinates of the mat 220A is registered in the obstacle database 151, and that the cleaning mode corresponding to the number of stacks is avoidance driving.

As shown in FIG. 14, the plan generation unit 130 generates a travel route PP1 by recalculating the travel route PP shown in FIG. 13 so as to avoid the mat 220A. The control unit 140 causes the autonomous vacuum cleaner 100 to resume traveling by controlling the drive unit 170 so as to travel along the travel route PP1 generated by the plan generation unit 130.

Note that when the autonomous vacuum cleaner 100 runs around the mat 220A, the turning point, in other words, the closest distance between the autonomous vacuum cleaner 100 and the mat 220A may be arbitrary. Any distance may be determined in advance. In this specific example, a position 30 cm away from a position corresponding to the lower side of bounding box BB shown in FIG. 12 is set as a turning point.

[Effects etc.]
As described above, the autonomous vacuum cleaner 100 according to the embodiment is an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space. The autonomous vacuum cleaner 100 includes a detection unit 110 that detects obstacle information 151F indicating the state of obstacles around the autonomous vacuum cleaner 100, and a detection unit 110 that detects obstacle information 151F indicating the state of obstacles around the autonomous vacuum cleaner 100, and a detection unit 110 that detects whether the autonomous vacuum cleaner 100 is stuck on an obstacle. Based on the stack detection unit 190 that detects the obstacle, the obstacle information 151F detected by the detection unit 110, and the detection result detected by the stack detection unit 190, the autonomous vacuum cleaner 100 is stuck on the obstacle. Obstacle management unit 120 calculates the number of times and associates obstacle information 151F and number of times information 151E indicating the number of stacks and stores it in storage unit 150, and based on obstacle information 151F and number of times information 151E, autonomous A control unit 140 that controls the traveling vacuum cleaner 100 is provided. Specifically, the control unit 140 controls the suction unit 160, the drive unit 170, and the cleaning unit 180 based on the obstacle database 151 calculated by the obstacle management unit 120 and the travel pattern database 152.

According to this, the autonomous vacuum cleaner 100 can change the way it moves toward an obstacle depending on the number of times it gets stuck. Thereby, the autonomous vacuum cleaner 100 can, for example, perform an operation to overcome an obstacle that can be overcome without getting stuck depending on the travel route without avoiding it. In this way, the autonomous vacuum cleaner 100 can implement a cleaning method that is optimal for each obstacle.

For example, the autonomous vacuum cleaner 100 further includes a plan generation unit 130 that generates plan information indicating a cleaning mode of the autonomous vacuum cleaner 100 based on the obstacle information 151F and the number of times information 151E. The control unit 140 controls the autonomous vacuum cleaner 100 based on the plan information generated by the plan generation unit 130.

According to this, the control unit 140 can clean each obstacle by driving the suction unit 160, the driving unit 170, and the cleaning unit 180 using a cleaning method that is optimal for each obstacle based on the plan information.

Furthermore, for example, the plan generation unit 130 generates plan information in which a travel route for the autonomous vacuum cleaner 100 is determined based on the obstacle information 151F and the number of times information 151E. Specifically, for example, the plan generation unit 130 generates plan information that determines the driving method of the drive unit 170 based on the number of times information 151E.

For example, the autonomous vacuum cleaner 100 further includes a suction unit 160 that sucks dirt. In this case, the plan generation unit 130 generates plan information in which the suction force of the suction unit 160 (specifically, the rotation speed of the suction motor 161) is determined based on the obstacle information 151F and the number of times information 151E.

For example, the autonomous vacuum cleaner 100 further includes a brush motor 181 that drives a brush (for example, the main brush 50). In this case, the plan generation unit 130 generates plan information in which the number of rotations of the brush motor 181 is determined based on the obstacle information 151F and the number of times information 151E.

According to these, the control unit 140 can clean each obstacle by driving the suction unit 160, the driving unit 170, and the cleaning unit 180 using a cleaning method that is more suitable for each obstacle based on the plan information.

For example, the obstacle information 151F includes the type of the obstacle (type information 151B), the color of the obstacle, the position of the obstacle in a predetermined space (position information 151C, 151D), and the size of the obstacle. At least one is shown as an aspect of the obstacle.

According to this, the control unit 140 cleans the suction unit 160, the drive unit, etc. using a cleaning method that is more optimal for each obstacle, such as whether the obstacle is likely to get stuck or difficult to get stuck depending on the type of the obstacle or the position of the obstacle. By controlling the cleaner 170 and the cleaning unit 180, the autonomous vacuum cleaner 100 can be caused to travel and clean.

Further, the method for controlling the autonomous vacuum cleaner 100 according to the embodiment is a method for controlling an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space, and includes the method for controlling an autonomous vacuum cleaner 100 that autonomously moves and cleans a predetermined space. An obstacle detection step (step S107) for detecting obstacle information 151F indicating the state of an object; a stuck detection step (step S105) for detecting whether the autonomous vacuum cleaner 100 is stuck on an obstacle; Based on the obstacle information 151F detected in the object detection step and the detection result detected in the stuck detection step, the number of times the object gets stuck is calculated, and the obstacle information 151F and the number of times information 151E indicating the number of stuck times are calculated. an obstacle management step (step S106) of linking and storing the information in the storage unit 150; a control step (step S103) of controlling the autonomous vacuum cleaner 100 based on the obstacle information 151F and the number of times information 151E; including.

According to this, the autonomous vacuum cleaner 100 can change the way it moves toward an obstacle depending on the number of times it gets stuck. Thereby, the autonomous vacuum cleaner 100 can, for example, perform an operation to overcome an obstacle that can be overcome without getting stuck depending on the travel route without avoiding it. In this way, the autonomous vacuum cleaner 100 can implement a cleaning method that is optimal for each obstacle.

Note that the present invention may be implemented as a program that causes a computer to execute the steps included in the method for controlling the autonomous vacuum cleaner 100 described above.

Specifically, the program according to the present invention is a program for causing a computer to execute a control method for an autonomous vacuum cleaner 100 that autonomously moves and cleans a predetermined space, An obstacle detection step that detects obstacle information indicating the state of surrounding obstacles, a stuck detection step that detects whether the autonomous vacuum cleaner 100 is stuck on an obstacle, and an obstacle detection step that detects obstacles detected in the obstacle detection step. Based on the obstacle information 151F and the detection result detected in the stuck detection step, the number of times the obstacle has been stuck is calculated, the obstacle information 151F is linked to the number of times information 151E indicating the number of times the object has been stuck, and the information is stored in the storage unit. 150; and a control step of controlling the autonomous vacuum cleaner 100 based on the obstacle information 151F and the number of times information 151E. This is a program to be executed.

According to this, the method for controlling the autonomous vacuum cleaner 100 according to the present embodiment can be easily executed by a computer.

Further, the present invention may be realized as a non-temporary recording medium such as a computer readable CD-ROM in which the above program is recorded. Further, the present invention may be realized as information, data, or a signal indicating the program. These programs, information, data, and signals may be distributed via a communication network such as the Internet.

(Modified example)
An autonomous vacuum cleaner according to a modified example will be described below with reference to FIGS. 15 to 17.

In addition, in the autonomous vacuum cleaner according to the modification described below, the same components as the autonomous vacuum cleaner 100 according to the embodiment are denoted by the same reference numerals, and some explanations are simplified or omitted. There are cases where

FIG. 15 is a block diagram showing a characteristic functional configuration of an autonomous vacuum cleaner 100a according to a modification.

The autonomous vacuum cleaner 100a includes a laser range finder 40, a camera 60, a depth sensor 70, a detection unit (obstacle detection unit) 110a, a stack detection unit 190, an obstacle management unit 120, and a plan generation unit. It includes a section 130a, a control section 140a, a storage section 150a, a suction section 160, a drive section 170, and a cleaning section 180.

The detection unit 110a is a processing unit that acquires sensor data detected by the various sensors included in the autonomous vacuum cleaner 100a, such as the laser range finder 40. The detection unit 110a is connected to the various sensors through control lines or the like so that sensor data can be obtained, for example.

Furthermore, the detection unit 110a detects whether a person is present in a predetermined space. In this embodiment, the obstacle type detection unit 111a detects whether or not a person is present in a predetermined space by analyzing the image obtained from the camera 60.

The plan generation unit 130a is a processing unit that generates a cleaning plan (plan information) indicating how the autonomous vacuum cleaner 100a will travel and clean a predetermined space.

Furthermore, the plan generation unit 130a generates plan information based on the detection result of the presence or absence of a person by the detection unit 110a.

The control unit 140a causes the autonomous vacuum cleaner 100 to autonomously travel in a predetermined space by controlling the suction unit 160, the drive unit 170, and the cleaning unit 180 based on the plan information generated by the plan generation unit 130. This is the processing section that cleans the area.

FIG. 16 is a diagram illustrating an example of the travel pattern database 153 held by the autonomous vacuum cleaner 100 according to the modification.

The travel pattern database 153 includes type information 152A, frequency information 152B, first cleaning mode information 153A, and second cleaning mode information 153B.

The first cleaning mode information 153A indicates that the autonomous vacuum cleaner 100 cleans the corresponding obstacle when the number of times the autonomous vacuum cleaner 100a gets stuck on the corresponding obstacle is the number of times the autonomous vacuum cleaner 100a gets stuck on the corresponding obstacle. This is information indicating the mode. The first cleaning mode information 153A is, for example, similar to the cleaning mode information 152C shown in FIG. 5.

In addition, the second cleaning mode information 153B indicates that when the number of times the autonomous vacuum cleaner 100a gets stuck on the corresponding obstacle is the number of times the autonomous vacuum cleaner 100a gets stuck on the corresponding obstacle, the number of times the autonomous vacuum cleaner 100 gets stuck on the corresponding obstacle is the number of times the autonomous vacuum cleaner 100 gets stuck on the corresponding obstacle. This is information that shows how to drive against.

Here, the second cleaning mode information 153B has a running pattern (that is, a cleaning mode) in which the autonomous vacuum cleaner 100a is less likely to get stuck on an obstacle than the first cleaning mode information 153A.

For example, when the detection unit 110a detects that a person is present in a predetermined space, the plan generation unit 130a generates plan information based on the first cleaning style information 153A, If it is detected that there is no such information, plan information is generated based on the second cleaning mode information 153B.

In the present embodiment, the first cleaning mode information 153A and the second cleaning mode information 153B only indicate the running method (running pattern), that is, the control method of the drive unit 170 controlled by the control unit 140a. However, information indicating the cleaning method, such as the number of rotations of the suction motor 161 included in the suction unit 160 controlled by the control unit 140a and the number of rotations of the brush motor 181 included in the cleaning unit 180 controlled by the control unit 140, is provided. May be included.

The control unit 140a controls the cleaning mode of the autonomous vacuum cleaner 100a based on the detection result of the presence or absence of a person by the detection unit 110a. More specifically, the control unit 140a controls the suction unit 160, the drive unit 170, and the cleaning unit based on the plan information generated by the plan generation unit 130 based on the detection result of the presence or absence of a person by the detection unit 110a. By controlling 180, the cleaning mode of the autonomous vacuum cleaner 100a is controlled.

For example, the plan generation unit 130 determines whether the control unit 140a starts controlling the autonomous vacuum cleaner 100a at a preset time, and generates plan information based on the determination result. For example, the user may use a so-called reservation function to have the autonomous vacuum cleaner 100a clean a predetermined space at a preset time. In this case, there is a high possibility that no user is present in the predetermined space at the timing when the autonomous vacuum cleaner 100a starts cleaning. Therefore, the plan generation unit 130 determines whether the control unit 140a starts controlling the autonomous vacuum cleaner 100a at a preset time, and controls the autonomous vacuum cleaner 100a at the preset time. If it is determined that this is the case, the control unit 140a generates plan information that causes the control unit 140a to control the cleaning mode based on the second cleaning mode information 153B, and the control unit 140a performs autonomous cleaning at a timing other than the preset time. When starting control of the machine 100a, plan information is generated to cause the control unit 140a to control the cleaning mode based on the first cleaning mode information 153A. In other words, the plan generation unit 130 determines whether or not reservation information indicating that the autonomous vacuum cleaner 100a will start operating at a preset time is received, and based on the determination result, the plan generation unit 130 creates a plan based on the determination result. Generate information.

The autonomous vacuum cleaner 100a also has an operation section such as a button or a touch panel for receiving reservation information from the user indicating that the autonomous vacuum cleaner 100a will start operating at a preset time, or an operation section such as a touch panel or the like. It may also include a communication interface or the like for acquiring the time via communication from a computer such as a smartphone operated by the user.

Furthermore, the autonomous vacuum cleaner 100a may include a clock unit such as an RTC (Real Time Clock) for measuring time.

Various processing units such as the detection unit 110a, the obstacle management unit 120, the plan generation unit 130a, and the control unit 140a, for example, include a control program for executing the above-described processing, and a CPU for executing the control program. It is realized from. Various processing units such as the detection unit 110a, the obstacle management unit 120, the plan generation unit 130a, and the control unit 140a may be realized by one or more CPUs.

The storage unit 150a is a memory that stores an obstacle database 151 and a driving pattern database 153. The storage unit 150a is realized by, for example, an HDD, a flash memory, or the like. Furthermore, the storage unit 150 stores, for example, control programs executed by various processing units such as the control unit 140a.

Note that, like the autonomous vacuum cleaner 100 shown in FIG. , a main brush 50, a camera 60, and a depth sensor 70.

[Processing procedure]
Next, the processing procedure of the autonomous vacuum cleaner 100a will be explained. The first example described below shows a processing procedure when the autonomous vacuum cleaner 100a detects the presence or absence of a person in a predetermined space. Further, a second example described below shows a processing procedure in the case of determining whether or not the autonomous vacuum cleaner 100a has started operating at a predetermined time.

<First example>
FIG. 17 is a flowchart showing a first example of processing executed by the autonomous vacuum cleaner 100a according to the modification. More specifically, FIG. 17 is a flowchart showing details of the traveling method selection process (step S206) shown in FIG. The autonomous vacuum cleaner 100a, for example, similarly to the autonomous vacuum cleaner 100, cleans a predetermined space by executing the flowcharts shown in FIGS. 5 to 7.

As shown in FIG. 17, the plan generation unit 130a first determines whether the detection unit 110a has detected a person (step S401).

When determining that the detection unit 110a has detected a person (Yes in step S401), the plan generation unit 130a selects the first cleaning mode information 153A included in the driving pattern database 153 (step S402).

On the other hand, when the detection unit 110a determines that the detection unit 110a does not detect a person (No in step S401), the plan generation unit 130a selects the second cleaning mode information 153B included in the driving pattern database 153 (step S403).

Next, the plan generation unit 130a selects and determines the cleaning mode for the obstacle based on the cleaning mode information selected in step S402 or step S403 and the number of times information 151E acquired in step S204 shown in FIG. (Step S404).

<Second example>
FIG. 18 is a flowchart showing a second example of processing executed by the autonomous vacuum cleaner 100a according to the modification. More specifically, FIG. 18 is a flowchart showing details of the traveling method selection process (step S206) shown in FIG. 7. The autonomous vacuum cleaner 100a, for example, similarly to the autonomous vacuum cleaner 100, cleans a predetermined space by executing the flowcharts shown in FIGS. 5 to 7.

As shown in FIG. 18, first, the plan generation unit 130a determines whether or not the autonomous vacuum cleaner 100a starts cleaning at a predetermined time, that is, based on the reservation information, the plan generation unit 130a It is determined whether or not it is a scheduled run for the vacuum cleaner 100a to start cleaning (step S501). For example, the plan generation unit 130a determines whether reservation information is being accepted.

When determining that the trip is not a reserved trip (No in step S501), the plan generation unit 130a selects the first cleaning mode information 153A included in the trip pattern database 153 (step S502).

On the other hand, if the plan generation unit 130a determines that the trip is a reserved trip (Yes in step S501), the plan generation unit 130a selects the second cleaning mode information 153B included in the trip pattern database 153.

Next, the plan generation unit 130a selects and determines a cleaning mode for the obstacle based on the cleaning mode information selected in step S502 or step S503 and the number of times information 151E acquired in step S204 shown in FIG. (Step S504).

[Effects etc.]
As described above, the autonomous vacuum cleaner 100a according to the modified example is an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space. The autonomous vacuum cleaner 100a includes a detection unit 110a that detects obstacles around the autonomous vacuum cleaner 100, and a stuck detection unit 190 that detects whether the autonomous vacuum cleaner 100 is stuck on an obstacle. , based on the obstacle information 151F indicating the state of the obstacle detected by the detection unit 110 and the detection result detected by the stuck detection unit 190, calculate the number of times the autonomous vacuum cleaner 100a got stuck on the obstacle. An obstacle management unit 120 calculates and associates the obstacle information 151F with frequency information 151E indicating the number of stacks and stores it in the storage unit 150a, and based on the obstacle information 151F and frequency information 151E, an autonomous driving type A control unit 140a that controls the vacuum cleaner 100a is provided. Furthermore, the autonomous vacuum cleaner 100a includes a plan generation unit 130a that generates plan information indicating a cleaning mode of the autonomous vacuum cleaner 100 based on the obstacle information 151F and the frequency information 151E. The control unit 140a controls the autonomous vacuum cleaner 100a based on the plan information generated by the plan generation unit 130a. Specifically, the control unit 140a controls the suction unit 160, the drive unit 170, and the cleaning unit 180 based on the obstacle database 151 calculated by the obstacle management unit 120 and the travel pattern database 153. The detection unit 110a included in the autonomous vacuum cleaner 100a according to the modification further detects whether or not there is a person in a predetermined space. The plan generation unit 130a generates plan information based on the detection result of the presence or absence of a person by the detection unit 110a.

For example, when a person is present in a predetermined space and the autonomous vacuum cleaner 100a gets stuck, there is a possibility that the person can change the stuck state to the non-stuck state. On the other hand, when the autonomous vacuum cleaner 100a gets stuck when no one is present in the predetermined space, there is a high possibility that the stuck state will not be changed to the non-stuck state and cleaning will not be performed for a long time. Therefore, the autonomous vacuum cleaner 100a determines the cleaning mode based on whether or not there is a person in a predetermined space, thereby searching for the optimal cleaning mode for obstacles and stacking. This prevents the state of not being cleaned for a long time to continue.

For example, the plan generation unit 130a included in the autonomous vacuum cleaner 100a according to the modification determines whether the control unit 140a starts controlling the autonomous vacuum cleaner 100a at a preset time, Plan information is generated based on the determination result.

For example, when a reserved run is executed, there is a high possibility that there are no people in the predetermined space. On the other hand, if the autonomous vacuum cleaner 100a starts operating instead of scheduled travel, there is a high possibility that the autonomous vacuum cleaner 100a was operated by a person in the predetermined space. There is a high possibility that there are. Therefore, the autonomous vacuum cleaner 100a determines the cleaning mode based on whether or not a scheduled run has been executed, thereby searching for the optimal cleaning mode for the obstacle and preventing the cleaning from getting stuck. Prevents the condition from continuing for a long time.

(Other embodiments)
Although the autonomous vacuum cleaner and the like according to the present invention have been described above based on the above embodiments and modifications, the present invention is not limited to the above embodiments and modifications.

Further, for example, in the above embodiment, it has been explained that the processing units such as the plan generation unit and the control unit included in the autonomous vacuum cleaner are realized by the CPU and the control program, respectively. For example, each of the components of the processing unit may be composed of one or more electronic circuits. Each of the one or more electronic circuits may be a general-purpose circuit or a dedicated circuit. The one or more electronic circuits may include, for example, a semiconductor device, an IC (Integrated Circuit), an LSI (Large Scale Integration), or the like. An IC or LSI may be integrated into one chip or into multiple chips. Here, it is called an IC or LSI, but the name changes depending on the degree of integration, and may be called a system LSI, VLSI (Very Large Scale Integration), or ULSI (Ultra Large Scale Integration). Furthermore, an FPGA (Field Programmable Gate Array) that is programmed after the LSI is manufactured can also be used for the same purpose.

Further, general or specific aspects of the invention may be implemented in a system, apparatus, method, integrated circuit, or computer program product. Alternatively, the computer program may be realized by a computer-readable non-temporary recording medium such as an optical disk, an HDD (Hard Disk Drive), or a semiconductor memory in which the computer program is stored. Further, the present invention may be realized by any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

In addition, forms obtained by applying various modifications to the embodiments and modifications that those skilled in the art can think of, or by arbitrarily combining the components and functions of the embodiments without departing from the spirit of the present invention. The present invention also includes such forms.

INDUSTRIAL APPLICABILITY The present invention can be widely used in autonomous vacuum cleaners that clean while autonomously moving.

10 Main body 11 Suction port 20 Wheel 30 Side brush 40 Laser range finder 50 Main brush 60 Camera 70 Depth sensor 100, 100a Autonomous vacuum cleaner 110, 110a Detection unit (obstacle detection unit)
111, 111a Obstacle type detection section 112 Distance detection section 113 Relative position detection section 114 Self-position detection section 115 Obstacle coordinate detection section 120 Obstacle management section 130, 130a Plan generation section 140, 140a Control section 150, 150a Storage section 151 Obstacle database 151A ID information 151B, 152A Type information 151C, 151D Position information 151E, 152B Number of times information 151F Obstacle information 152, 153 Traveling pattern database 152C Cleaning mode information 153A First cleaning mode information 153B Second cleaning mode information 160 Suction section 161 Suction motor 170 Drive section 171 Wheel motor 180 Cleaning section 181 Brush motor 190 Stack detection section 200 Image 210 Charger 220A, 220B Mat 230 Predicted passing line CA Predetermined space PP, PP1 Travel route T Trajectory V Shooting range BB Bounding box

Claims (10)

An autonomous vacuum cleaner that autonomously moves and cleans a predetermined space,
an obstacle detection unit that detects obstacle information indicating a state of obstacles around the autonomous vacuum cleaner;
a stuck detection unit that detects whether the autonomous vacuum cleaner is stuck on the obstacle;
Based on the obstacle information detected by the obstacle detection unit and the detection result detected by the stack detection unit, calculate the number of times the autonomous vacuum cleaner gets stuck on the obstacle, and an obstacle management unit that associates the obstacle information with the number of times information indicating the number of stuck times and stores it in a storage unit;
a control unit that controls the autonomous vacuum cleaner based on the obstacle information and the number of times information ,
The control unit determines that the obstacle corresponding to the obstacle information is a predetermined type of obstacle when the number of stacks indicated by the number of times information linked to the obstacle information detected by the obstacle detection unit is 1 or more. When this is the case, the autonomous vacuum cleaner is controlled to operate in a cleaning mode different from that when the number of stacks is 0 and to move over the obstacle without avoiding it.
Autonomous vacuum cleaner. Furthermore, a plan generation unit that generates plan information indicating a cleaning mode of the autonomous vacuum cleaner based on the obstacle information and the number of times information,
The autonomous vacuum cleaner according to claim 1, wherein the control unit controls the autonomous vacuum cleaner based on the plan information generated by the plan generation unit. The autonomous vacuum cleaner according to claim 2, wherein the plan generation unit generates the plan information that determines a travel route for the autonomous vacuum cleaner based on the obstacle information and the number of times information. . Furthermore, it is equipped with a suction section that sucks out dirt.
The autonomous vacuum cleaner according to claim 2 or 3, wherein the plan generation unit generates the plan information that determines the suction force of the suction unit based on the obstacle information and the number of times information. Furthermore, it is equipped with a brush motor that drives the brush.
The autonomous driving type according to any one of claims 2 to 4, wherein the plan generation unit generates the plan information in which the number of rotations of the brush motor is determined based on the obstacle information and the number of times information. Vacuum cleaner. The obstacle detection unit further detects whether or not there is a person in the predetermined space,
The autonomous vacuum cleaner according to any one of claims 2 to 5, wherein the plan generation unit generates the plan information based on the detection result of the presence or absence of a person by the obstacle detection unit. The plan generation unit determines whether the control unit starts controlling the autonomous vacuum cleaner at a preset time, and generates the plan information based on the determination result. 6. The autonomous vacuum cleaner according to any one of 6. The obstacle information indicates at least one of the type of the obstacle, the color of the obstacle, the position of the obstacle in the predetermined space, and the size of the obstacle as a mode of the obstacle. The autonomous vacuum cleaner according to any one of claims 1 to 7. A method of controlling an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space,
an obstacle detection step of detecting obstacle information indicating a state of obstacles around the autonomous vacuum cleaner;
a stuck detection step of detecting whether the autonomous vacuum cleaner is stuck on the obstacle;
Based on the obstacle information detected in the obstacle detection step and the detection result detected in the stuck detection step, calculate the number of times the object gets stuck with respect to the obstacle, and indicate the obstacle information and the number of times the object gets stuck. an obstacle management step of storing the information in a storage unit in association with the number of times;
a control step of controlling the autonomous vacuum cleaner based on the obstacle information and the number of times information,
In the control step, if the number of stacks indicated by the number of times information associated with the obstacle information detected in the obstacle detection step is 1 or more, the obstacle corresponding to the obstacle information is a predetermined type of obstacle. When this is the case, the autonomous vacuum cleaner is controlled to operate in a cleaning mode different from that when the number of stacks is 0 and to move over the obstacle without avoiding it.
How to control an autonomous vacuum cleaner. A program for causing a computer to execute a control method for an autonomous vacuum cleaner that autonomously moves and cleans a predetermined space,
an obstacle detection step of detecting obstacle information indicating a state of obstacles around the autonomous vacuum cleaner;
a stuck detection step of detecting whether the autonomous vacuum cleaner is stuck on the obstacle;
Based on the obstacle information detected in the obstacle detection step and the detection result detected in the stuck detection step, calculate the number of times the object gets stuck with respect to the obstacle, and indicate the obstacle information and the number of times the object gets stuck. an obstacle management step of storing the information in a storage unit in association with the number of times;
a control step of controlling the autonomous vacuum cleaner based on the obstacle information and the number of times information,
In the control step, if the number of stacks indicated by the number of times information associated with the obstacle information detected in the obstacle detection step is 1 or more, the obstacle corresponding to the obstacle information is a predetermined type of obstacle. , the autonomous vacuum cleaner controls the autonomous vacuum cleaner to operate in a cleaning mode different from that when the number of stacks is 0 and to move over the obstacle without avoiding it. A program that causes a computer to execute a control method.

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